Feature Extractor for the Classification of Approved Halal Logo in Malaysia / Khairul Muzzammil Saipullah, Nurul Atiqah Ismail and Yewguan Soo

By:

Khairul Muzzammil Saipullah

Contributor(s):

Material type: Text

TextPublication details: New Jersey : The Institute of Electrical and Electronics Engineers 2012Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781467331432

Subject(s):

Online resources:

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Summary: This paper present a new feature extractors called the Fractionalized Principle Magnitude (FPM) that is evaluated in the classification of approved Halal logo with respect to classification accuracy and time consumptions. Feature can be classified into two group; global feature and local feature. In this study, several feature extractors have been compared with the proposed method such as histogram of gradient (HOG), Hu moment, Zernike moment and wavelet co-occurrence histogram (WCH). The experiments are conducted on 50 different approved Halal logos. The result shows that proposed FPM method achieves the highest accuracy with 90.4% whereas HOG, Zernike moment, WCH and Hu moment achieve 75.2%, 64.4%, 47.2% 44.4% of accuracies, respectively.

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Conference Paper	Halal Development Corporation Library Open Shelves	CONF 000062 (Browse shelf(Opens below))	Available

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Previous	CONF 000059 Factor of Awareness in Searching and Sharing of Halal Food Product among Muslim Families in Malaysia /	CONF 000060 Factors Affecting Muslim Students Awareness of Halal Products in Yogyakarta, Indonesia /	CONF 000061 Fakta Ilmiah Tentang Keharaman Babi /	CONF 000062 Feature Extractor for the Classification of Approved Halal Logo in Malaysia /	CONF 000063 Food Ingredient, Religiousity and Halal Compliance among Food Truck Traders in Klang Valley /	CONF 000064 GAPS IN THE LEGISLATION HALAL IN MALAYSIA : A STUDY OF LITERATURE /	CONF 000066 Brand, Identity and Reputation: Exploring, Creating New Realities and Fresh Perspectives on Multi-Sensory Experiences	Next

This paper present a new feature extractors called the Fractionalized Principle Magnitude (FPM) that is evaluated in the classification of approved Halal logo with respect to classification accuracy and time consumptions. Feature can be classified into two group; global feature and local feature. In this study, several feature extractors have been compared with the proposed method such as histogram of gradient (HOG), Hu moment, Zernike moment and wavelet co-occurrence histogram (WCH). The experiments are conducted on 50 different approved Halal logos. The result shows that proposed FPM method achieves the highest accuracy with 90.4% whereas HOG, Zernike moment, WCH and Hu moment achieve 75.2%, 64.4%, 47.2% 44.4% of accuracies, respectively.

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